Computer Engineering student focused on Data Analytics and Data Engineering.
I design structured data workflows using Python and SQL, build ETL pipelines, and develop AI-powered analytics systems.
Core technologies I work with:
- Python (Pandas, data processing)
- SQL (CTEs, window functions, analytical queries)
- PostgreSQL / SQLite
- ETL pipeline development
- Relational schema design
- Interactive analytics applications
AI-powered analytics application that converts natural language questions into SQL queries and generates charts and business insights.
Key highlights:
- Built a natural-language-to-SQL system using LLMs
- Implemented SQL safety validation
- Built an interactive Streamlit analytics interface
- Generated automated data visualizations and insights
Tech stack:
Python · PostgreSQL · Streamlit · OpenAI · SQLAlchemy · Plotly
Repository:
https://github.com/Brightpmk/ecommerce-ai-analytics-assistant
Designed a modular ETL pipeline that processes relational e-commerce datasets.
Key highlights:
- Extracted data from 8 relational datasets (100k+ records)
- Performed data cleaning and validation
- Loaded datasets into SQLite
- Built analytical fact table (
fact_order_item_sales)
Tech stack:
Python · Pandas · SQL · SQLite
Repository:
https://github.com/Brightpmk/olist-etl-pipeline
Exploratory data analysis project focused on revenue trends, logistics performance, and customer behavior.
Key highlights:
- Processed 100k+ transaction records
- Built analytical datasets using SQL and Python
- Created visualizations to analyze marketplace performance
- Identified revenue concentration patterns across product categories
Tech stack:
Python · Pandas · SQL · Matplotlib · Jupyter Notebook
Repository:
https://github.com/Brightpmk/Basic_data-analysis-project_00
Areas I am currently strengthening:
- Advanced SQL querying
- Data transformation using Pandas
- ETL pipeline architecture
- Relational schema design
- Backend data workflows
- AI-assisted analytics systems
- Data warehouse design (fact & dimension modeling)
- Incremental data loading strategies
- SQL performance optimization
- Workflow orchestration concepts (Airflow)
- Cloud fundamentals (AWS / Linux)
To become a data-focused engineer capable of designing scalable data pipelines, analytics systems, and AI-driven data platforms.